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# coding=utf-8
# Copyright 2024 HuggingFace Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys
import tempfile
import unittest
import numpy as np
import torch
from transformers import AutoTokenizer, CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer, T5EncoderModel
from diffusers import (
AutoencoderKL,
FlowMatchEulerDiscreteScheduler,
SD3Transformer2DModel,
StableDiffusion3Pipeline,
)
from diffusers.utils.testing_utils import is_peft_available, require_peft_backend, require_torch_gpu, torch_device
if is_peft_available():
from peft import LoraConfig
from peft.utils import get_peft_model_state_dict
sys.path.append(".")
from utils import check_if_lora_correctly_set # noqa: E402
@require_peft_backend
class SD3LoRATests(unittest.TestCase):
pipeline_class = StableDiffusion3Pipeline
def get_dummy_components(self):
torch.manual_seed(0)
transformer = SD3Transformer2DModel(
sample_size=32,
patch_size=1,
in_channels=4,
num_layers=1,
attention_head_dim=8,
num_attention_heads=4,
caption_projection_dim=32,
joint_attention_dim=32,
pooled_projection_dim=64,
out_channels=4,
)
clip_text_encoder_config = CLIPTextConfig(
bos_token_id=0,
eos_token_id=2,
hidden_size=32,
intermediate_size=37,
layer_norm_eps=1e-05,
num_attention_heads=4,
num_hidden_layers=5,
pad_token_id=1,
vocab_size=1000,
hidden_act="gelu",
projection_dim=32,
)
torch.manual_seed(0)
text_encoder = CLIPTextModelWithProjection(clip_text_encoder_config)
torch.manual_seed(0)
text_encoder_2 = CLIPTextModelWithProjection(clip_text_encoder_config)
text_encoder_3 = T5EncoderModel.from_pretrained("hf-internal-testing/tiny-random-t5")
tokenizer = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
tokenizer_2 = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip")
tokenizer_3 = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-t5")
torch.manual_seed(0)
vae = AutoencoderKL(
sample_size=32,
in_channels=3,
out_channels=3,
block_out_channels=(4,),
layers_per_block=1,
latent_channels=4,
norm_num_groups=1,
use_quant_conv=False,
use_post_quant_conv=False,
shift_factor=0.0609,
scaling_factor=1.5035,
)
scheduler = FlowMatchEulerDiscreteScheduler()
return {
"scheduler": scheduler,
"text_encoder": text_encoder,
"text_encoder_2": text_encoder_2,
"text_encoder_3": text_encoder_3,
"tokenizer": tokenizer,
"tokenizer_2": tokenizer_2,
"tokenizer_3": tokenizer_3,
"transformer": transformer,
"vae": vae,
}
def get_dummy_inputs(self, device, seed=0):
if str(device).startswith("mps"):
generator = torch.manual_seed(seed)
else:
generator = torch.Generator(device="cpu").manual_seed(seed)
inputs = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 5.0,
"output_type": "np",
}
return inputs
def get_lora_config_for_transformer(self):
lora_config = LoraConfig(
r=4,
lora_alpha=4,
target_modules=["to_q", "to_k", "to_v", "to_out.0"],
init_lora_weights=False,
use_dora=False,
)
return lora_config
def test_simple_inference_with_transformer_lora_save_load(self):
components = self.get_dummy_components()
transformer_config = self.get_lora_config_for_transformer()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
pipe.transformer.add_adapter(transformer_config)
self.assertTrue(check_if_lora_correctly_set(pipe.transformer), "Lora not correctly set in transformer")
inputs = self.get_dummy_inputs(torch_device)
images_lora = pipe(**inputs).images
with tempfile.TemporaryDirectory() as tmpdirname:
transformer_state_dict = get_peft_model_state_dict(pipe.transformer)
self.pipeline_class.save_lora_weights(
save_directory=tmpdirname,
transformer_lora_layers=transformer_state_dict,
)
self.assertTrue(os.path.isfile(os.path.join(tmpdirname, "pytorch_lora_weights.safetensors")))
pipe.unload_lora_weights()
pipe.load_lora_weights(os.path.join(tmpdirname, "pytorch_lora_weights.safetensors"))
inputs = self.get_dummy_inputs(torch_device)
images_lora_from_pretrained = pipe(**inputs).images
self.assertTrue(check_if_lora_correctly_set(pipe.transformer), "Lora not correctly set in transformer")
self.assertTrue(
np.allclose(images_lora, images_lora_from_pretrained, atol=1e-3, rtol=1e-3),
"Loading from saved checkpoints should give same results.",
)
def test_simple_inference_with_transformer_lora_and_scale(self):
components = self.get_dummy_components()
transformer_lora_config = self.get_lora_config_for_transformer()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
output_no_lora = pipe(**inputs).images
pipe.transformer.add_adapter(transformer_lora_config)
self.assertTrue(check_if_lora_correctly_set(pipe.transformer), "Lora not correctly set in transformer")
inputs = self.get_dummy_inputs(torch_device)
output_lora = pipe(**inputs).images
self.assertTrue(
not np.allclose(output_lora, output_no_lora, atol=1e-3, rtol=1e-3), "Lora should change the output"
)
inputs = self.get_dummy_inputs(torch_device)
output_lora_scale = pipe(**inputs, joint_attention_kwargs={"scale": 0.5}).images
self.assertTrue(
not np.allclose(output_lora, output_lora_scale, atol=1e-3, rtol=1e-3),
"Lora + scale should change the output",
)
inputs = self.get_dummy_inputs(torch_device)
output_lora_0_scale = pipe(**inputs, joint_attention_kwargs={"scale": 0.0}).images
self.assertTrue(
np.allclose(output_no_lora, output_lora_0_scale, atol=1e-3, rtol=1e-3),
"Lora + 0 scale should lead to same result as no LoRA",
)
def test_simple_inference_with_transformer_fused(self):
components = self.get_dummy_components()
transformer_lora_config = self.get_lora_config_for_transformer()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
output_no_lora = pipe(**inputs).images
pipe.transformer.add_adapter(transformer_lora_config)
self.assertTrue(check_if_lora_correctly_set(pipe.transformer), "Lora not correctly set in transformer")
pipe.fuse_lora()
# Fusing should still keep the LoRA layers
self.assertTrue(check_if_lora_correctly_set(pipe.transformer), "Lora not correctly set in transformer")
inputs = self.get_dummy_inputs(torch_device)
ouput_fused = pipe(**inputs).images
self.assertFalse(
np.allclose(ouput_fused, output_no_lora, atol=1e-3, rtol=1e-3), "Fused lora should change the output"
)
def test_simple_inference_with_transformer_fused_with_no_fusion(self):
components = self.get_dummy_components()
transformer_lora_config = self.get_lora_config_for_transformer()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
output_no_lora = pipe(**inputs).images
pipe.transformer.add_adapter(transformer_lora_config)
self.assertTrue(check_if_lora_correctly_set(pipe.transformer), "Lora not correctly set in transformer")
inputs = self.get_dummy_inputs(torch_device)
ouput_lora = pipe(**inputs).images
pipe.fuse_lora()
# Fusing should still keep the LoRA layers
self.assertTrue(check_if_lora_correctly_set(pipe.transformer), "Lora not correctly set in transformer")
inputs = self.get_dummy_inputs(torch_device)
ouput_fused = pipe(**inputs).images
self.assertFalse(
np.allclose(ouput_fused, output_no_lora, atol=1e-3, rtol=1e-3), "Fused lora should change the output"
)
self.assertTrue(
np.allclose(ouput_fused, ouput_lora, atol=1e-3, rtol=1e-3),
"Fused lora output should be changed when LoRA isn't fused but still effective.",
)
def test_simple_inference_with_transformer_fuse_unfuse(self):
components = self.get_dummy_components()
transformer_lora_config = self.get_lora_config_for_transformer()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
inputs = self.get_dummy_inputs(torch_device)
output_no_lora = pipe(**inputs).images
pipe.transformer.add_adapter(transformer_lora_config)
self.assertTrue(check_if_lora_correctly_set(pipe.transformer), "Lora not correctly set in transformer")
pipe.fuse_lora()
# Fusing should still keep the LoRA layers
self.assertTrue(check_if_lora_correctly_set(pipe.transformer), "Lora not correctly set in transformer")
inputs = self.get_dummy_inputs(torch_device)
ouput_fused = pipe(**inputs).images
self.assertFalse(
np.allclose(ouput_fused, output_no_lora, atol=1e-3, rtol=1e-3), "Fused lora should change the output"
)
pipe.unfuse_lora()
self.assertTrue(check_if_lora_correctly_set(pipe.transformer), "Lora not correctly set in transformer")
inputs = self.get_dummy_inputs(torch_device)
output_unfused_lora = pipe(**inputs).images
self.assertTrue(
np.allclose(ouput_fused, output_unfused_lora, atol=1e-3, rtol=1e-3), "Fused lora should change the output"
)
@require_torch_gpu
def test_sd3_lora(self):
"""
Test loading the loras that are saved with the diffusers and peft formats.
Related PR: https://github.com/huggingface/diffusers/pull/8584
"""
components = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
lora_model_id = "hf-internal-testing/tiny-sd3-loras"
lora_filename = "lora_diffusers_format.safetensors"
pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
pipe.unload_lora_weights()
lora_filename = "lora_peft_format.safetensors"
pipe.load_lora_weights(lora_model_id, weight_name=lora_filename)
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